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- VK is one of the most visited platforms on the Russian internet. We create products that we, our friends, family and 97 million other people use every day. We believe that it’s important to share our experience, support talented specialists and develop IT education in Russia. - VK’s mission is to connect people, services and companies by creating simple and convenient communication tools. - We set and achieve ambitious goals. We demand a lot of ourselves since the result of our work is used by our friends, family and millions of others. Every day brings new challenges, and we welcome these because they help us grow. - We create products that we're proud of. It's not easy, but that's what makes it interesting. We're always on the lookout for specialists who are ready to take on challenges, go above and beyond and think about others, from users to their colleagues. - The VK Team is based in three cities: Saint Petersburg, Moscow, and Sochi, with regional representative offices in Yekaterinburg, Kazan, Nizhny Novgorod, and Kazakhstan. - Our headquarters is located in the Singer House, a historical landmark in the heart of Saint Petersburg. Among ourselves, we call the building "Zinger" and "Zee". When people ask, we usually just say that it’s the "House of Books". The Singer House is where we meet to discuss the most important decisions. - VK also has offices in the U Krasnogo Mosta business center, where the product and technical teams are based. - Since mid-2020, we have been building teams that work fully remotely from anywhere they want. This makes it possible for people from cities without VK offices to join the team.

VK.com

Switch to English sign up · Sign in · Sign up · Mobile app · Recommendation technologies used · Posts by Mukhammad · Search · Cancel · Mukhammad Fayzoda · 5 Jan at 8:04 pm · photo updated

Mukhammad Fayzoda | VK

Spotify is marktleider, en toch schrapt het 17 procent van ...

I conducted a lot of interesting and effective experiments. Some of them increased our metrics significantly, some of them less. I left the company several days ago, but I want to share something interesting. Today I will tell you how I managed to increase the user engagement of VK Messenger by adding a block of recommendations of contacts. And what is the most important – I will honestly tell you about the problems I faced and how I solved them because I think that is the most interesting part of stories like this one. The block of recommendations for contacts is located at the top of the chat list in VK Messenger. Here are the steps I took to integrate this feature: First of all, I studied the existing messengers and saw similar features in the products of our competitors like Facebook and Instagram, but I wasn't sure if it would fit well with VK Messenger. I had a hypothesis that it could increase the engagement of our users, so I decided to give it a try. In the beginning, I launched this feature based on a heuristical algorithm: people whom you added as friends recently, phone contacts, those with whom you chatted earlier, etc. I would like to conduct a RAT (riskiest assumptions test) in the cheapest possible way to make sure I don't spend a lot of resources on things I am not sure would give us meaningful results. While I was sure that this MVP was really viable, I invited the machine learning team to help us improve the recommendations. Eventually, when I launched this feature based on an ML algorithm on the Web version, I got a +1% increase in time spent and +0.72% in the number of sent messages. I launched it on our Android and iOS mobile applications. Now, here are the problems I faced in developing and launching this experiment: Our team uses the messenger for day-to-day work, and they were concerned if this block with recommended contacts would distract the users from normal communication in existing chats. I collected feedback from our colleagues and did user interviews to improve the user experience. As a result, I decreased the size of the block a little bit and added a toggle to turn off these recommendations for those who don't like them. Also, it's possible to hide certain people from the list if you like the recommendations but don't want to see some of them. Later, when I launched an experiment on our real users I recognized that this opinion was biased because our core audience consisted of young scholars and students, and they found it entertaining to see new people to chat with. I have different types of users, so the switching off feature wasn't distracting for our core users, but also it was well-received by those who wanted to see just their chat list without recommendations. When I launched this experiment on the Android version I saw a slight initial decrease in time spent on the Stories feature, but the total time spent on the platform increased. This happened because the number of users who spent their time chatting with recommended contacts grew significantly. So I decided that this feature should be launched for 100% of our users. Later, I would improve the metrics of Stories by adding the Stories feature to the Messenger Tab, making it a win-win for both of us. When I launched this experiment on iOS, our support team came to us with complaints that the users don't understand what it is and what type of people they see in this block. In just one night our developer created a chatbot that explained the algorithm and asked for feedback so that I could make it even better. I launched it the next day, placing it in the first position of the block with recommended contacts. To sum it up, let me point out the most important parts of the development process: It's important to launch the feature within your team first – eat your own dog food! Collect the problems and ideas on how to improve them, then try to solve these problems. Be careful about biased opinions, though. Conduct as many user interviews as possible to ensure you don't miss any important moments. Launch only on the part of the users first, get feedback, and fix the problems fast. Then launch it step by step, gradually increasing the percentage of the users who see this feature. Always try to find the most effective solutions that need less effort. It will help you move forward faster. Check that you don't screw up other parts of the whole service. And if you see that something is going wrong, find the correct way to calculate profits and losses to make the right decision. Be brave! Changing the parts of your service that weren't changed for years may seem risky at times, but being bold and fast is the only way to create a big impact.

How to Add Contact Recommendations to VK Messenger

With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google, GE Healthcare, Siemens Healthineers, and many other leading AI startups, these AI dreams are achieving more feasibility. However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements. With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google , GE Healthcare , Siemens Healthineers , and many other leading AI startups, these AI dreams are achieving more feasibility. 46 percent statistical concern Google GE Healthcare Siemens Healthineers However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. GE Healthcare Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. Siemens Healthineers According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” VKTR A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. host of other startups Can we trust AI with our healthcare data? Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. Definitive identified invasive This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. paper A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” GDPR and CCPA There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements.

As Healthcare AI Advances, How Do we Balance the Benefits With Privacy Concerns?

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Welcome to our brand new series - The HackerNoon Company of the Week, where we feature the top tech brands from our tech company database, making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike.This week, we present our business blogging partner__inDrive__ (former inDriver, short for “Independent Drivers“) - a ride-hailing service with one big edge from its competitors: the fare for the trip is set by the passengers themselves! Who doesn't like autonomy and self sovereignty ✊ Wanna be part of HackerNoon’s evergreen tech company database? Request Your Tech Company Page on HackerNoon! Meet inDrive: #FunFact Did you know that inDrive started as a small community of independent drivers in Yakutsk? According to the inDrive's ex-CTO: "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive.In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." From a small community of independent drivers to one of the top ride hailing services known globally, inDrive has come a long way! inDrive’s Featured Business Stories on HackerNoon inDrive.tech has shared some amazing content via the HackerNoon Business Blogging Program, and has generated 3 months 9 days 6 hours and 36 minutes of reading time! Here are our top 3 favorite stories by InDrive: Understanding Dynamic Programming So You Can Use It Effectively
How We Automated the Verification of Car Photos
How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook That's all this week, hackers!Stay Creative, Stay Iconic.The__HackerNoon Team__ Welcome to our brand new series - The HackerNoon Company of the Week, where we feature the top tech brands from our tech company database , making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. This week, we present our business blogging partner__ inDrive __ (former inDriver, short for “Independent Drivers“) - a ride-hailing service with one big edge from its competitors: the fare for the trip is set by the passengers themselves! Who doesn't like autonomy and self sovereignty ✊ tech company database tech company database inDrive Wanna be part of HackerNoon’s evergreen tech company database? Request Your Tech Company Page on HackerNoon! Wanna be part of HackerNoon’s evergreen tech company database? Request Your Tech Company Page on HackerNoon ! Request Your Tech Company Page on HackerNoon Meet inDrive: #FunFact Did you know that inDrive started as a small community of independent drivers in Yakutsk? According to the inDrive's ex-CTO : inDrive's ex-CTO inDrive's ex-CTO "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive.In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive.In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive. In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." From a small community of independent drivers to one of the top ride hailing services known globally, inDrive has come a long way! inDrive’s Featured Business Stories on HackerNoon inDrive.tech has shared some amazing content via the HackerNoon Business Blogging Program , and has generated 3 months 9 days 6 hours and 36 minutes of reading time! Here are our top 3 favorite stories by InDrive: inDrive.tech HackerNoon Business Blogging Program Understanding Dynamic Programming So You Can Use It Effectively How We Automated the Verification of Car Photos How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook Understanding Dynamic Programming So You Can Use It Effectively Understanding Dynamic Programming So You Can Use It Effectively How We Automated the Verification of Car Photos How We Automated the Verification of Car Photos How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook That's all this week, hackers! Stay Creative, Stay Iconic. The__ HackerNoon Team __ HackerNoon Team

Meet inDrive: The HackerNoon Company of the Week

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Lernen Sie inDrive kennen: Das HackerNoon-Unternehmen der Woche

Conozca inDrive: la empresa de la semana de HackerNoon

Rencontrez inDrive : l'entreprise HackerNoon de la semaine

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inDrive のご紹介: 今週の HackerNoon 企業

inDrive에서 만나보세요: 금주의 HackerNoon 회사

Conheça inDrive: a empresa HackerNoon da semana

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结识 inDrive：本周 HackerNoon 公司

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HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today! Tell us about yourself! I’m a Senior Product Designer at PolyAI, focusing on building a conversational AI platform to help callers solve their issues without wasting time waiting for an available agent. Within the last nine years, I made a transition from Graphic design to UI/UX design at a design agency and then to Product design and had the pleasure of working with various great tech-led companies such as SberTech, Gett, and SpatialChat. Before the design field, I studied accounting and developed a passion for data and analytics that help me work closer with a business right now. Why did you choose this field in the first place? I had quite a rough and eventful path with the wrong career decision before I entered this field. At the time, when I should’ve selected the course at the university, I didn’t think that I could select something else apart from accounting because it was pretty straightforward, and I gave up on following my dream to become a designer. A few years later, when I wrote my work for a master’s degree and had a few years of working experience as an accountant, I understood that I didn’t love what I was doing and the only thing I loved to do was design that I learned as a hobby. It took half a year to get my first job as a Graphic designer, and then I dived into UI/UX design out of curiosity. During my first few years as a UI/UX designer at an agency, I was extremely interested in helping businesses reach their goals via interfaces but working in an agency as an outsource designer didn’t allow me to dive into business problems that deep. I made one more tiny transition from UI/UX design into Product design that enabled my full potential. I always had a passion for the design field, and by being a Product designer, I’m not just a person who designs interfaces but also helps users with their needs and assists a business in reaching goals with any technical constraints. For the last six years of work as a Product designer, I’m still extremely excited about my work and love this rapidly evolving field. What tech are you most excited/passionate about right now and why? I'm totally excited about AI and machine learning. The way these technologies are transforming everyday life and bringing simpler and smarter solutions to companies is fascinating. As a part of the AI startup, I witnessed how this technology saves companies money and helps callers faster than a human. Also, I use a lot of AI tools that already improved my working process and helped me to save time for more crucial parts of my work. This is an exciting time to live in, and I can’t wait to see what else this field can bring us in the future. What tech are you most worried about right now and why? The same as excitement, I’m most worried about AI and the ethical implications, as well as potential fraud via this powerful technology. This technology is very advanced and accessible without severe legal restrictions and regulations right now. There have already been a few cases of fraud that involved AI technology. Since there aren't many tools that can detect and prevent that kind of misuse of AI, this field could bring risks to individual privacy and data security. What are your hobbies and interests outside of tech? I’m an absolute video game fan. I love how video games create the ambiance via sounds, stories, visuals, and actions. Besides exploring new worlds, checking my reflexes, and just having fun, I use this activity as a source of inspiration for my work on interfaces. I’m in love with this field and have the same passion as I am with product design. I even tried CG art, so I have massive respect and admiration for everyone involved in game development. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? The biggest challenges I've faced as a woman in tech are fighting my impostor syndrome and biases, which led me to a lower salary rate, lack of confidence, and stagnation in my career growth. All of that caused situations where I've often found myself silent or ignored by colleagues when other people confidently shared potentially risky ideas. Regarding the impostor syndrome, it pushed me to spend many hours learning the design basis, psychology, and everything else that is directly connected to my work to prove that I’m not an impostor and have enough knowledge. It boosted my career and confidence a lot when other designers started asking for advice, a few lessons, or mentorship.All this hard work and recognition from other designers made me confident, so I started taking the initiative in conversations, pitching my ideas, and exceeding expectations when someone underestimated my skills for certain reasons. Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? Unfortunately, I faced many stories worth sharing, but I’ll skip the most common ones when the ideas I pitched were ignored, and when a male colleague shared the same idea a few moments later, it was a “breakthrough idea.” One situation happened in one of the latest companies I worked for; a Product manager I worked with constantly allowed abusive and respectless comments about the work of female colleagues with attempts to cut off reasonable ideas. As you can guess, the dialogues with male colleagues were full of respect and trust in their professionalism without any doubts in pitched ideas. The climax of this situation for me was when this Product manager, in a very rude form with an accent in my gender, declined my design solution without any arguments on a group call. After that call, I initiated a dialog with this product manager where I questioned the situation, and the only answer I received was a joking response that I had misunderstood. Unfortunately, the actions I made didn't help the situation, nor did chats with my direct lead and HR department. This situation taught me to stop silently ignoring such respectless behavior and ask a person a reason for that behavior or elaborate on the decision even when they commented on someone else. Eventually, it helped, and other colleagues started noticing the change, but it took a few months. This story is not an attempt to show the negatives but to highlight the way out of it. What was the biggest setback/failure that you faced, and how did you manage it? My biggest setback was spending years studying accounting and even getting a master’s degree. This decision delayed my career and made me work twice as hard to become a designer. Also, this episode of my life shows me what I’m really capable of and that I definitely should pursue my passion. What's your biggest achievement that you're really proud of? My biggest achievement is the skills I've developed during this fight with my impostor syndrome and pivot in my career. All the doubts in myself I had in the past transformed into solid knowledge and experience that I’m more than happy to share with other designers or people who would like to change their careers. As a result of all this, I have received recognition and respect from other designers I was lucky to work with, and I was given the opportunity to guide my design vision in multiple projects. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? In my opinion, the gender gap in the tech industry is the consequence of many stereotypes and expectations regarding social roles for women that were set a long time ago. All these stereotypes and expectations follow women in every sphere, especially at work in the tech industry.Unfortunately, I witnessed situations when highly intelligent women with an interest in tech gave up or didn’t try to enter the industry just because they couldn’t find enough support but heard a lot of stereotypes that tech is not for women. It is especially frustrating when there are not that many women who share their stories to inspire others. To reduce this gender gap, we should continue fighting the stereotypes and misogyny and encourage women to pursue their interests in the tech industry. Also, I strongly believe that having various communities such as “Women in Tech” that provide support, share success stories, and show role models in these fields is extremely important at any stage of a career. Apart from these steps, there must be work done in the educational field to encourage young girls to explore other options because there is still a perception that technology, programming, engineering, and physics are for men. Who is your tech idol? Why? I couldn’t say that I have an idol in the tech industry, but I have a person who has inspired me a lot on my career path. I’m talking about amazingly intelligent and skilled Sasha Ermolenko, who is currently the Director of the Product Design Department at VK, which is one the biggest social networks in Russia. Each time, she offers unique insights about the design sphere, shares her strong leadership skills and expresses her thoughts about the gender gap in the design sphere. Do you have any advice for aspiring girls who want to join the field?** Yes, your career path could be tough for you in this field, but don’t give up and be afraid to follow your dreams. All your current skills, unique perspectives, interests, and strengths are already your greatest assets in this dynamic industry.Text me if you need support! HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today ! this interview series with women in tech Share your story today Tell us about yourself! I’m a Senior Product Designer at PolyAI, focusing on building a conversational AI platform to help callers solve their issues without wasting time waiting for an available agent. Within the last nine years, I made a transition from Graphic design to UI/UX design at a design agency and then to Product design and had the pleasure of working with various great tech-led companies such as SberTech, Gett, and SpatialChat. Before the design field, I studied accounting and developed a passion for data and analytics that help me work closer with a business right now. Why did you choose this field in the first place? Why did you choose this field in the first place? I had quite a rough and eventful path with the wrong career decision before I entered this field. At the time, when I should’ve selected the course at the university, I didn’t think that I could select something else apart from accounting because it was pretty straightforward, and I gave up on following my dream to become a designer. A few years later, when I wrote my work for a master’s degree and had a few years of working experience as an accountant, I understood that I didn’t love what I was doing and the only thing I loved to do was design that I learned as a hobby. It took half a year to get my first job as a Graphic designer, and then I dived into UI/UX design out of curiosity. During my first few years as a UI/UX designer at an agency, I was extremely interested in helping businesses reach their goals via interfaces but working in an agency as an outsource designer didn’t allow me to dive into business problems that deep. I made one more tiny transition from UI/UX design into Product design that enabled my full potential. I always had a passion for the design field, and by being a Product designer, I’m not just a person who designs interfaces but also helps users with their needs and assists a business in reaching goals with any technical constraints. For the last six years of work as a Product designer, I’m still extremely excited about my work and love this rapidly evolving field. What tech are you most excited/passionate about right now and why? I'm totally excited about AI and machine learning. The way these technologies are transforming everyday life and bringing simpler and smarter solutions to companies is fascinating. As a part of the AI startup, I witnessed how this technology saves companies money and helps callers faster than a human. Also, I use a lot of AI tools that already improved my working process and helped me to save time for more crucial parts of my work. This is an exciting time to live in, and I can’t wait to see what else this field can bring us in the future. What tech are you most worried about right now and why? What tech are you most worried about right now and why? The same as excitement, I’m most worried about AI and the ethical implications, as well as potential fraud via this powerful technology. This technology is very advanced and accessible without severe legal restrictions and regulations right now. There have already been a few cases of fraud that involved AI technology. Since there aren't many tools that can detect and prevent that kind of misuse of AI, this field could bring risks to individual privacy and data security. What are your hobbies and interests outside of tech? I’m an absolute video game fan. I love how video games create the ambiance via sounds, stories, visuals, and actions. Besides exploring new worlds, checking my reflexes, and just having fun, I use this activity as a source of inspiration for my work on interfaces. I’m in love with this field and have the same passion as I am with product design. I even tried CG art, so I have massive respect and admiration for everyone involved in game development. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? The biggest challenges I've faced as a woman in tech are fighting my impostor syndrome and biases, which led me to a lower salary rate, lack of confidence, and stagnation in my career growth. All of that caused situations where I've often found myself silent or ignored by colleagues when other people confidently shared potentially risky ideas. Regarding the impostor syndrome, it pushed me to spend many hours learning the design basis, psychology, and everything else that is directly connected to my work to prove that I’m not an impostor and have enough knowledge. It boosted my career and confidence a lot when other designers started asking for advice, a few lessons, or mentorship. All this hard work and recognition from other designers made me confident, so I started taking the initiative in conversations, pitching my ideas, and exceeding expectations when someone underestimated my skills for certain reasons. Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? Unfortunately, I faced many stories worth sharing, but I’ll skip the most common ones when the ideas I pitched were ignored, and when a male colleague shared the same idea a few moments later, it was a “breakthrough idea.” One situation happened in one of the latest companies I worked for; a Product manager I worked with constantly allowed abusive and respectless comments about the work of female colleagues with attempts to cut off reasonable ideas. As you can guess, the dialogues with male colleagues were full of respect and trust in their professionalism without any doubts in pitched ideas. The climax of this situation for me was when this Product manager, in a very rude form with an accent in my gender, declined my design solution without any arguments on a group call. After that call, I initiated a dialog with this product manager where I questioned the situation, and the only answer I received was a joking response that I had misunderstood. Unfortunately, the actions I made didn't help the situation, nor did chats with my direct lead and HR department. This situation taught me to stop silently ignoring such respectless behavior and ask a person a reason for that behavior or elaborate on the decision even when they commented on someone else. Eventually, it helped, and other colleagues started noticing the change, but it took a few months. This story is not an attempt to show the negatives but to highlight the way out of it. What was the biggest setback/failure that you faced, and how did you manage it? What was the biggest setback/failure that you faced, and how did you manage it? My biggest setback was spending years studying accounting and even getting a master’s degree. This decision delayed my career and made me work twice as hard to become a designer. Also, this episode of my life shows me what I’m really capable of and that I definitely should pursue my passion. What's your biggest achievement that you're really proud of? What's your biggest achievement that you're really proud of? My biggest achievement is the skills I've developed during this fight with my impostor syndrome and pivot in my career. All the doubts in myself I had in the past transformed into solid knowledge and experience that I’m more than happy to share with other designers or people who would like to change their careers. As a result of all this, I have received recognition and respect from other designers I was lucky to work with, and I was given the opportunity to guide my design vision in multiple projects. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? In my opinion, the gender gap in the tech industry is the consequence of many stereotypes and expectations regarding social roles for women that were set a long time ago. All these stereotypes and expectations follow women in every sphere, especially at work in the tech industry. Unfortunately, I witnessed situations when highly intelligent women with an interest in tech gave up or didn’t try to enter the industry just because they couldn’t find enough support but heard a lot of stereotypes that tech is not for women. It is especially frustrating when there are not that many women who share their stories to inspire others. To reduce this gender gap, we should continue fighting the stereotypes and misogyny and encourage women to pursue their interests in the tech industry. Also, I strongly believe that having various communities such as “Women in Tech” that provide support, share success stories, and show role models in these fields is extremely important at any stage of a career. Apart from these steps, there must be work done in the educational field to encourage young girls to explore other options because there is still a perception that technology, programming, engineering, and physics are for men. Who is your tech idol? Why? I couldn’t say that I have an idol in the tech industry, but I have a person who has inspired me a lot on my career path. I’m talking about amazingly intelligent and skilled Sasha Ermolenko, who is currently the Director of the Product Design Department at VK, which is one the biggest social networks in Russia. Each time, she offers unique insights about the design sphere, shares her strong leadership skills and expresses her thoughts about the gender gap in the design sphere. Do you have any advice for aspiring girls who want to join the field?** Yes, your career path could be tough for you in this field, but don’t give up and be afraid to follow your dreams. All your current skills, unique perspectives, interests, and strengths are already your greatest assets in this dynamic industry. Text me if you need support!

Elena Shabanova, Senior Product Designer at PolyAI: Women in Tech Interview

This paper is under CC 4.0 license. available on arxiv Authors: (1) Kota Chin, University of Tsukuba, National Institute of Information and Communications Technology Japan; (2) Keita Emura, Kanazawa University, Japan National Institute of Information and Communications Technology Japan; (3) Kazumasa Omote, University of Tsukuba National Institute of Information and Communications Technology Japan. Table of Links Abstract & Introduction Preliminaries Proposed Anonymous Yet Accountable Contract Wallet System Implementation Conclusion & References III. PROPOSED ANONYMOUS YET ACCOUNTABLE CONTRACT WALLET SYSTEM In this section, we describe the proposed anonymous yet accountable contract wallet system. First, we classify users who issue transactions. • Group User: a user i who manages a key pair of the underlying accountable ring signature scheme (pki ,ski) and the common opening key osk. i ∈ R denotes when user i is a group member where R is the ring used for signature verification (i.e., pki ∈ R). • Individual User: a user j who manages own ECDSA key pair (vkj ,sigkj ). A. Security Requirements We require the following security notions hold. Especially, due to the provability and proving soundness, the protocol is accountable because they allow us to clarify the accountability involved in issuing a transaction. • Anonymity: All entities that can observe transactions including contract wallets and excluding group users and nodes that communicate with the transaction issuer, cannot identify the actual issuer among the verification key holders contained in ring R. • Unforgeability: As long as all signatures (either/both the ring signatures and/or the ECDSA signatures) specified by the Policy are sent to a contract wallet, no entity can issue a transaction that is valid under the Policy. • Provability: When a group user i sends a ring signature to issue a transaction, the user can generate its proof. • Proving Soundness: When a group user i does not send a ring signature to issue a transaction, the user cannot generate its proof. B. High-level Description In the following, we explain the case of Policy := {R, j}. First, we assume that all group users belonging to R share osk. Let a group user ∈ R issue a transaction. Then, the user i generates a ring signature Σ, and the individual user j generates an ECDSA signature σ, respectively. After these signatures are sent to a contract wallet, the contract wallet checks whether Σ is valid under R, and σ is valid under vkj . If both signatures are valid, then the contract wallet runs the transaction. We note that only the signature verification is executed on-chain. Next, the user runs the Open algorithm using osk, generates π that is a proof of transaction issuing, and sends π to other group users (via an off-chain channel). For example, any information sharing tool used in the organization (e.g., a bulletin board in the organization or e-mails) can be used for sending pi. Other group users can recognize that the user i issues the transaction by checking π using the Judge algorithm. i i We can easily consider a case that consents of two or more individual users are required. For example, Policy := {R, j, k}. Similarly, we can consider two or more rings, e.g., R1 and R2. Here, we assume that R1 ∩ R2 = ∅ and do not consider the case R1 ∩ R2 6= ∅ because the contract wallet cannot distinguish whether one user who belongs to R1 ∩ R2 6= ∅ sends two ring signatures or not, due to anonymity. Similarly, we do not consider a case that consents two or more group users belonging to the same ring are required because the contract wallet cannot distinguish whether one user who belongs to the ring sends two or more ring signatures or not, due to anonymity. We remark that ring members can internally check whether one user who belongs to the ring sends two or more ring signatures or not. Thus, in the case that the proof π will be opened after issuing the transaction, these restrictions could be removed. C. Proposed System Here, we describe the proposed anonymous yet accountable contract wallet system. The proposed system consists of two procedures, DeployContractWallet and SecdTransaction. The DeployContractWallet protocol is used to deploy a contract wallet, and the SecdTransaction protocol issues a transaction against a transaction issuing request. • DeployContractWallet(Code, BlockChain): Let Code be the code of a contract wallet. In the protocol, Code is sent to nodes that support account abstraction. Here, we describe BlockChain as the set of nodes. Then, Code contains a policy Policy, and a set of verification keys (a ring R and ECDSA verification keys), and the rule that specifies the procedure run after the verification of signatures is passed. Finally, deploy a contract wallet CW. • SecdTransaction(TransactionData, BlockChain, CW): A transaction data TransactionData, containing accountable ring signatures and ECDSA signatures, is sent to CW via nodes described by BlockChain. Then, CW checks the validity of signatures according to Policy, and issues a transaction. In our system, the RVerify algorithm is run on-chain by the contract wallet, as in the case of ECDSA signatures, because any one can issue a transaction if no verification is involved. One may think that the RVerify algorithm could be run offchain, e.g., the operator of the underlying L2 system runs the RVerify algorithm and sends the validity result to the wallet. However, it requires to assume an additional trust to the operator D. Security Discussion • Anonymity: Due to account abstraction and the anonymity of the underlying accountable ring signature scheme, the fact that a group user belonging R generates the signature to issue a transaction is not leaked. i • Unforgeability: Due to the unforgeability of the underlying accountable ring signature scheme and ECDSA, no entity, that does not belong to R or does not have a signing key corresponding to vk specified by Policy, can issue a transaction. Due to the tamper resistance of the blockchain, the code of a contract wallet is not modified after its deployment. Thus, as long as all signatures (either/both ring signatures and/or the ECDSA signatures) specified by Policy are sent to a contract wallet, no entity can issue a transaction that is valid under the Policy. • Provability: Let Σ be a ring signature generated by ski and verified by a contract wallet. Due to the correctness of the underlying accountable ring signature scheme, Judge(opk, M, R, Σ, pk , π) = 1 holds. i • Proving Soundness: Let a ring signature be generated by ski . Due to the tracing soundness, no user (including group users) can produce π that is accepted by the Judge algorithm with pk 6= pk . i

Anonymous, Accountable Contract Wallet System With Account Abstraction: Proposed System

This paper is under CC 4.0 license. available on arxiv Authors: (1) Kota Chin, University of Tsukuba, National Institute of Information and Communications Technology Japan; (2) Keita Emura, Kanazawa University, Japan National Institute of Information and Communications Technology Japan; (3) Kazumasa Omote, University of Tsukuba National Institute of Information and Communications Technology Japan. Table of Links Abstract & Introduction Preliminaries Proposed Anonymous Yet Accountable Contract Wallet System Implementation Conclusion & References Abstract Account abstraction allows a contract wallet to initiate transaction execution. Thus, account abstraction is useful for preserving the privacy of externally owned accounts (EOAs) because it can remove a transaction issued from an EOA to the contract wallet and hides who issued the transaction by additionally employing anonymous authentication procedures such as ring signatures. However, unconditional anonymity is undesirable in practice because it prevents to reveal who is accountable for a problem when it arises. Thus, maintaining a balancing between anonymity and accountability is important. In this paper, we propose an anonymous yet accountable contract wallet system. In addition to account abstraction, the proposed system also utilizes accountable ring signatures (Bootle et al., ESORICS 2015). The proposed system provides (1) anonymity of a transaction issuer that hides who agreed with running the contract wallet, and (2) accountability of the issuer, which allows the issuer to prove they agreed with running the contract wallet. Moreover, due to a security requirement of accountable ring signatures, the transaction issuer cannot claim that someone else issued the transaction. This functionality allows us to clarify the accountability involved in issuing a transaction. In addition, the proposed system allows an issuer to employ a typical signature scheme, e.g., ECDSA, together with the ring signature scheme. This functionality can be considered an extension of the common multi-signatures that require a certain number of ECDSA signatures to run a contract wallet. The proposed system was implemented using zkSync (Solidity). We discuss several potential applications of the proposed system, i.e., medical information sharing and asset management. —Blockchain, Account abstraction, Contract wallet, Accountable ring signatures. Index Terms I. INTRODUCTION A. Introduction of Account Abstraction Ethereum involves two kinds of accounts, i.e., externally owned accounts (EOA), which are controlled by a usermanaged secret key, and contract accounts (contract wallets), which are controlled by smart contracts. In the current implementation of Ethereum, to run a contract wallet, an EOA must send a transaction to the contract wallet. Then the contract wallet runs a transaction according to the rule specified in the contract. Account abstraction [11] allows a contract wallet to initiate transaction execution, i.e., it can remove a transaction sent from an issuer to the contract. Account abstraction provides two primary benefits. The first benefit is the reduction of gas costs because account abstraction can remove a transaction sent from an issuer to the contract wallet. The second benefit is flexible verification. In the current Ethereum implementation, transactions, issued by EOAs, are verified according to the validity of signatures generated by secret keys of EOAs, and the underlying signature scheme is restricted to the elliptic curve digital signature algorithm (ECDSA). Thus, ECDSA signatures are employed generally when transaction validity is verified in the contract, although, theoretically, any signature schemes can be employed because signatures are verified by programs. In contrast, due to account abstraction, no EOA issues transactions, and thus any signature schemes can be employed more easily to verify transaction validity in the contract. For example, CRYSTALS-Dilithium [15], FALCON [18], and SPHINCS+ [4], which have been selected by the NIST Post-Quantum Cryptography Standardization, can be employed under the assumption they can be implemented by a program language that can be run by the contract wallet, e.g., Solidity.[1] In addition, account abstraction allows us to employ signatures with rich functionalities, such as accountable ring signatures [10]. Representative examples of systems that support account abstraction include StarkNet [2] and zkSync [3], which are network technologies in Ethereum Layer 2 (L2). Note that L2 is described in further detail in Section II-B. Transactions between the EOAs and contract wallets are removed; thus account abstraction is useful for preserving the privacy of EOAs. In fact, EIP-2938 [11] states that “Privacypreserving systems like tornado.cash” are a motivation to introduce account abstraction (tornado.cash is a mixing service). Concretely, it is expected that account abstraction can hide the issuer of a transaction. Note that a contract wallet typically verifies a signature using a verification key that has been registered in the contract program. Since the verification key is public (in a public blockchain), anyone can identify who issued the transaction (precisely, which verification key is used and the key holder who issued the transaction). In other words, account abstraction does not hide transaction issuer information. We emphasize that unconditional anonymity could promote crime. For example, tornado.cash is a tool that could be misused to facilitate money laundering, and the Fiscal Information and Investigation Service, an agency of the government of the Netherlands, arrested a 29-year-old man in Amsterdam as a suspected developer [1]. This indicates maintaining a balancing between anonymity and accountability is important. B. Privacy on Ethereum Each EOA manages an ECDSA verification key vk, and the corresponding address is a (last 20 bytes of) hashed value of vk (here Keccak-256 hash function is used). Here, as each address appears random,[2] anyone can easily determine whether multiple transactions were issued by the same EOA, although it is difficult to detect the EOA in the real world. This means that issuer privacy is preserved in the sense of pseudonymity in crypto asset trading. When crypto assets are traded between individuals, the pseudonymity level privacy protection might be sufficient. However, some information leakage occurs if a system is implemented using smart contracts. Does Pseudonymity Provide Sufficient Privacy Protection? For example, assume that a request to view patient data is made by a transaction in an electronic medical record system using Ethereum smart contracts [5], [16], [17], [20], [22], [25], [26], [30], [33]. In this case, we assume that multipatient data are accessed from the same address. Then, we expect that the transaction issuing address is managed by a doctor or researcher. In addition, we expect that patients are suffering from similar diseases. Here, if the doctor or researcher associated with the address is identifiable, information identifying the patient’s disease may be leaked (although this was not authorized by the patient) depending on the specialty of the issuer of the transaction. Thus, no information about hospitals and medical offices should be leaked from transactions. However, it is necessary to internally verify who issued the transaction in order to prevent unnecessary access to medical data. In the case of group asset management, pseudonymity level privacy protection would make the investment performance of each address public. Thus, it is desirable to be able to prove the investment performance internally while keeping information about the issuer of the transaction secret externally. Let a pair of signing and verification keys be generated and let all members of a group share the key pair. When a member issues a transaction, the same signing key is used to generate a signature. Then, anonymity level privacy protection is provided because a contract wallet uses the same verification key to verify the signature. However, there is no way to identify who issued the transaction, even among group members. Unconditional anonymity is undesirable in practice because it prevents to reveal who is accountable for a problem when it arises. In addition, determining how to revoke the signing key when a member leaves the group is a nontrivial problem. A Naive Solution 1: Key Sharing. Amazon Web Services (AWS) Key Management Service or a system on permissioned blockchain that support a trusted execution environment (TEE), e.g., Intel SGX, can be used to solve the above problems. By using such services, group members can issue a transaction without sharing the signing key. Here, anonymity still holds because the contract wallet uses the same verification key. In addition, group members can internally identify who issued a transaction via an access log (AWS Key Management Service) or the transaction (permissioned blockchain). Member revocation is also possible via access control to the services. Thus, technically we can provide both anonymity (for outside of the group) and accountability (for the group) simultaneously. However, both methods assume trust in AWS and TEE, which is undesirable relative to increase trust points. A Naive Solution 2: External Services. C. Our Contribution In this paper, we focus on account abstraction, which is attractive in terms of constructing a privacy-preserving system, and we propose an anonymous yet accountable contract wallet system (Figure 1).[3] In addition to account abstraction, the proposed system employs accountable ring signatures [10]. [4] We implemented the proposed system using zkSync (Solidity). Precisely, we implemented the verification algorithm of the underlying accountable ring signature scheme using Solidity. The proposed system is briefly introduced as follows. • To issue a transaction, a ring signature is sent to a contract wallet, and the contract wallet verifies the signature. Then, the contract wallet uses a ring (i.e., a set of verification keys) to verify the signature. Anonymity holds in the sense that the contract wallet cannot identify the issuer among the verification key holders. • The opening functionality of the accountable ring signatures allows the actual issuer to prove that they issued the transaction, and other users can recognize this fact. In contrast, due to a security requirement of accountable ring signatures (i.e., opening soundness), no user can prove that they issued a transaction if they did not actually issue the transaction, and moreover the transaction issuer cannot claim that someone else issued the transaction In addition, the proof can be generated by other ring members who share the secret opening key of the underlying accountable ring signatures. This functionality allows us to clarify the accountability involved in issuing a transaction. We remark that we mainly focus on how to identify/prove who issued a transaction in this paper. That is, we assume that the organization to which the transaction issuer belongs is accountable, and we do not address what penalties that organization would impose on the transaction issuer if the transaction became problematic, or how that organization would be held accountable. • The proposed system allows an issuer to employ a typical signature scheme, e.g., ECDSA, with the ring signature scheme. This functionality can be considered an extension of the typical multi-signatures that require a certain number of ECDSA signatures to run a contract wallet. For example, the contract wallet determines whether one of the issuers belongs to a group (in an anonymous manner), and a specific user (in the usual manner) agrees to run the contract wallet by verifying a ring signature and an ECDSA signature. This is illustrated in Figure 2. In the usual multi-signatures setting, anyone can recognize who agreed with running the contract wallet by observing ECDSA verification keys used for ECDSA signatures verification. Note that if two signatures are sent to the contract wallet separately, one of them must be preserved in the contract wallet which incurs an additional gas cost. To reduce the cost, one sender can collect all of the required signatures, and send them to the contract wallet. D. Applications Potential applications of the proposed system include, but are not limited to, the following. As mentioned previously, when a request to view patient data is made by a transaction in an electronic medical record system using Ethereum smart contracts, it is highly desirable that no information about hospitals and medical offices is leaked from the transactions. Medical Information Sharing. However, it is also essential to be able to internally verify who issued the transaction in order to prevent unnecessary access to medical data. With the proposed system, from the outside, it is possible to hide who is accessing patient data among members in an organization. For example, when a hospital is specified as the organization, it is possible to keep secret which doctors’ offices have access to medical data. If a clinical department, e.g., internal medicine, psychosomatic medicine, or plastic surgery, is specified as the organizational unit, which department is related to which disease is leaked, but no other information is leaked because which doctor accessed the patient data is kept secret. In addition, dividing the organization into independent departments relative to the actual medical offices may further prevent unnecessary leakage of information. Thus, by setting organizational units appropriately, we can control leaked information. In addition, more flexible settings can be realized by using our extended multi-signatures. For example, we can consider a case where the hospital director or a department head must agree to issue a transaction. Here, one person in a medical office needs to agree to issue the transaction. The range of blockchain-based asset management has expanded due to the advent of smart contracts. Thus, the number of investment companies that manage their clients’ funds has been increasing. In some cases, the addresses held by lenders are known publicly. Then, the status of the fund management can be checked.[5] It is assumed that there are cases where multiple users share an EOA as the address, and cases where multiple users employ contract wallets where transactions can be issued with the consent of a certain number of users. In the former case, it is impossible to know who issued the transaction internally. In the latter case, due to the pseudonymity level privacy protection, anyone can check the investment performance of each address. Except for cases where the investment status is disclosed intentionally, such information can be leaked unexpectedly. By employing the proposed system, the investment status of each address is not disclosed externally, and the investment status of each individual can be known internally. Asset Management. [1] Although we consider only signatures in this paper, any verification method can be employed or a contract wallet with no verification step can be constructed. [2] Vanity addresses are often used to reduce the storage cost of addresses. For example, using an address 0x0000..., a part of address “0· · · 0” does not need to be stored. Even in this case, pseudonymity level privacy protection is guaranteed. [3] We used Free Clip Art (http://www.clker.com/). [4] A user (EOA) needs to issue a transaction to a contract wallet when account abstraction is not employed. Thus, information that who issued a transaction is leaked even a ring signature scheme is employed in this case. Thus, both account abstraction and ring signatures are required mandatory in our system. We do not the following one-time deployment case that a contract wallet is newly deployed for issuing a transaction because it is not realistic solution. [5] In fact, these addresses are not disclosed by lenders intentionally. Here, they are assumed to be lenders’ addresses based on the asset management status.

Anonymous, Accountable Contract Wallet System With Account Abstraction: Abstract & Intro

This paper is available on arxiv under CC 4.0 license. Authors: (1) Amit Kumar Chatterjee, Yukawa Institute for Theoretical Physics, Kyoto University & Department of Physics, Ramakrishna Mission Vidyamandira; (2) Satoshi Takada, Department of Mechanical Systems Engineering and Institute of Engineering; (3) Hisao Hayakawa, Yukawa Institute for Theoretical Physics, Kyoto University. Table of Links Introduction Model and Protocol Different Regimes in d˜− Γ˜ Plain and Multiple QMPE QMPE in Region (d): Second-order Ep QMPE in Region (a1): Oscillations Summary Appendix A: Eigenvectors of L in region (d) Appendix B: Eigenvectors of L in region (a1) Appendix C: QMPE in region (b): purely exponential relaxation Appendix D: QMPE in region (e): 3rd order EP References V. QMPE IN REGION (a1): OSCILLATIONS In this section, we explore QMPE in region (a1) of Hatano’s model which contains complex eigenvalues with both non-zero real and imaginary parts. The eigenvalues are non-degenerate and they are denoted by 0, λ2, λ3 = λre+iλim, λ4 = λre−iλim, where the subscripts re and im correspond to real and imaginary parts, respectively. In addition, the eigenvalues satisfy the relation 0 < λ2 < λre meaning λ2 is the slowest relaxation mode. In contrast to the nontrivial approximate diagonalization [Eqs. (10)- (11)] for second-order EP in Sec. IV, here the complete diagonalization can be obtained through the usual method A. Density matrix elements The the individual density matrix elements ρj (t) (j = 1, 2, 3, 4) are given by To discuss QMPE in the density matrix, let us focus on the ground state probability i.e. ρgg(t). Of course, there is no algebraic time dependence in the relaxation observed in Sec. IV. Rather, the interesting part of the relaxation in region (a1) originates from the complex nature of the eigenvalues and the corresponding coefficients ak in Eq. (27). Specifically, we have found that a3 and a4 are complex conjugates of each other, they take the forms a3 = are + iaim and a4 = are − iaim. Using such expressions, we obtain the following formula for ρgg(t): In Fig. 7 we present an example of multiple QMPE in the ground state density matrix element ρgg(t), where we have chosen parameters that satisfy the QMPE criteria Eq. (30) for region (a1). We observe that the two copies I and II relax in oscillatory manner and intersect each other multiple times. The inset explicitly shows that the difference between the two copies ∆ρgg(t) intersects zero multiple times, confirming the multiple QMPE. Comparing Fig. 3(a) [region (d)] with the inset of Fig. 7 [region (a1)], we understand that the number of intersections in region (a1) is not restricted to two as in region (d). Therefore, in region (a1) one can have many more intersections and higher order QMPE compared to the maximum double QMPE in the region (d). Since the amplitudes of the oscillations can be controlled by tuning the initial driving strengths ˜dI and ˜dII, complex eigenvalues seem to be useful to obtain multiple intersections and thereby multiple QMPE. It would be important in the future to find the exact number of solutions to ∆ρgg(t) = 0 either analytically or numerically. B. Energy The formal expression for average energy E(t) is given by Eq. (18). In the previous Sec. IV, we have already shown that QMPE in ground state probability does not necessarily imply QMPE in energy, in general. However, it is possible to find control parameters that can give rise to QMPE in both observables, although the intersection time(s) are generally different. Following the same path, we would like to see if E(t) shows multiple QMPE for the same set of parameters used to achieve multiple QMPE for ρgg(t) in Fig. 7. Indeed, Fig. 8 exhibits multiple intersections between time evolved copies of energies starting from I and II, both relaxing towards the same final energy. The inset of Fig. 8 alternatively demonstrates the multiple QMPE through multiple intersections of the energy difference ∆E(t) = EI(t)−EII(t) with zero, remarkably different from Fig. 3(b) with a maximum of two such intersections in region (d). C. von Neumann entropy Next we consider the von Neumann entropy SvN(t) [Eq. (22)]. To observe QMPE in SvN(t), we consider the intersection times for the entropy difference between two copies ∆SvN(t) = S I vN − S II vN. In Fig. 9, we present the behavior of ∆SvN(t) for the same set of parameter values used in Figs. 7 and 8. Indeed, we observe that ∆SvN(t) intersects zero multiple times showing the existence of multiple QMPE in the von Neumann entropy. D. Temperature Here, we study the thermal QMPE in region (a1) using the system temperature defined in Eq. (23). In Fig. 10, we present the time evolution of the temperatures of two copies I and II, one initially hotter than the other. It is fascinating that the temperatures of both the copies relax in oscillatory manner towards the same steady state temperature and during the relaxations, they intersect each other multiple times, leading to multiple thermal QMPE. This, in turn, implies that the hotter and colder copies reverse their identities multiple times (after each intersection the previous hotter copy turns into colder copy and remains so until the next intersection). Such oscillatory multiple thermal QMPE is observed here for the first time in quantum systems (as far as our knowledge is concerned), although multiple thermal MPE in classical inertial suspensions has already been reported [42]. The physical interpretation and utility of multiple thermal QMPE have to be explored in future in more details. Note that we have used the same parameter set for multiple thermal QMPE as in Figs. 7-9. Similar to Ref. [65], here also we observe negative temperature [114– 118] in the early stage of relaxation. This is due to nonmonotonic natures of energy and entropy as functions of time. However, it is satisfying that even if we neglect such initial negative temperatures, we observe multiple QMPE at later times of the relaxation process where the temperatures of both the copies remain positive. E. Kullback-Leibler divergence In this subsection we explore the QMPE in KL divergence defined in Eq. (24). Since we have been able to obtain multiple QMPE in region (a1) in all other observables considered here, it is natural to ask about the possibility of multiple QMPE in DKL(t) as well. However, we have found that the KL divergence does not show any intersection between two copies I and II for the same parameter values used in Figs. 7-10. Therefore, we have to use different values of the initial drives (still within the fixed dissipation protocol) to achieve QMPE in DKL(t), presented in Fig. 11(a). Intriguingly, we have only observed single intersection between the two time evolved copies, resulting in single QMPE in KL divergence. This means that once the initially higher distant copy intersects the initially lower distant copy producing QMPE, thereafter the new lower distant copy (that was initially higher distant) remains closer to the steady state for the rest of the relaxation process. In fact, for all other parameter sets that we have checked, we could not find multiple QMPE in DKL(t). The proof of the possibility or impossibility of multiple QMPE in KL divergence in presence of complex eigenvalues remains unsolved and could be an important question to investigate later. In this scenario, it would be interesting to examine the corresponding behaviors of the speeds vKL(t) [Eq. (25)] of the two copies I and II. In Fig. 11(b), we present the time evolution of speeds for the same set of parameters that gave rise to single QMPE in DKL(t). It is fascinating to observe that the speeds, unlike the distances, intersect each other multiple times producing multiple QMPE. The first intersection between the speeds of the two copies [Fig. 11(b)] shows a time lag in comparison to the intersection between distances of the same copies [Fig. 11(a)]. Such a time lag between the QMPE in KL divergence and the speed is in agreement with the observation in region (d) [Fig. 6]. However, contrary to single QMPE in vKL(t) in region (d), we have obtained multiple QMPE in the speed in region (a1). The contrasting behavior of single QMPE in DKL(t) and multiple QMPE in vKL(t) imply that the initially higher distant copy that becomes lower distant after the intersection in distances, can remain at lower distance even if its speed sometimes become smaller than the speed of the current higher distant copy. This is interesting because the system almost behaves like an intelligent and efficient runner in a race who knows how to adjust the speed (i.e. sometimes running faster and sometimes running slower than the people behind) and still remains in the first position to finish the race.

Multiple Quantum Mpemba Effect: Exceptional Points and Oscillations: QMPE in Region (a1)

This paper is available on arxiv under CC 4.0 license. Authors: (1) Amit Kumar Chatterjee, Yukawa Institute for Theoretical Physics, Kyoto University & Department of Physics, Ramakrishna Mission Vidyamandira; (2) Satoshi Takada, Department of Mechanical Systems Engineering and Institute of Engineering; (3) Hisao Hayakawa, Yukawa Institute for Theoretical Physics, Kyoto University. Table of Links Introduction Model and Protocol Different Regimes in d˜− Γ˜ Plain and Multiple QMPE QMPE in Region (d): Second-order Ep QMPE in Region (a1): Oscillations Summary Appendix A: Eigenvectors of L in region (d) Appendix B: Eigenvectors of L in region (a1) Appendix C: QMPE in region (b): purely exponential relaxation Appendix D: QMPE in region (e): 3rd order EP References IV. QMPE IN REGION (d): SECOND-ORDER EP A. Density matrix elements Using Eqs. (10) and (11) in Eq. (1), we have obtained the following expressions of the density matrix elements ρj (t) (j = 1, 2, 3, 4): where the initial density matrix elements ρn(0) before quench are given by Eq. (5) and the elements ℓk,n are provided in Appendix A. Interestingly, in the presence of the second-order EP in region (d), the density matrix elements in Eq. (13) have picked up an additional algebraic term proportional to time t. This is remarkably different from the time evolution in regions (b) which has only purely exponential relaxations [see Appendix B] and region (a1) that contains damped oscillations [see Sec. V]. The linear t dependence originates from the off-diagonal defect in the Jordan normal form in Eq. (11). To investigate QMPE, we focus on the ground state density matrix element ρgg(t) (≡ ρ4(t)). To detect the QMPE, we would like to find the intersections of the two copies I and II during their relaxation, i.e. solution(s) to the equation ∆ρgg(t) = 0. We obtain the following form of ∆ρgg(t) where the co-efficients α1, α2, α3 are given by The solutions to such transcendental equation in Eq. (14) are given by the Lambert W function, where the equation yey = x has only one solution y = W0(x) if x > 0 and two solutions y = W0(x) and y = W−1(x) if −1/e 6 x < 0. Indeed, such properties of Lambert W function imply that there can be more than one solutions to ∆ρgg(t) = 0, meaning multiple QMPE. More precisely, at most two intersection times i.e. double QMPE can be observed in region (d). Below we provide the criteria for double QMPE and single QMPE in this region: The asymptotic expansions of the functions W0(x) and W−1(x) can be found in Ref. [105]. Note that Lambert W functions have found applications in several fields such as viscous flows [106], crystal growth [107], epitaxial film growth [108], phase separation of polymer mixtures [109], criticality in temporal networks [110] etc. In Fig. 1, we plot the solutions t˜0 and t˜−1 [Eq. (17)] as functions of the driving strengths before quench. Indeed, we observe that there are regions with single QMPE where only t˜0 exists and there are other regions where both solutions prevail leading to double QMPE. In particular, we observe double QMPE for large values of ˜dI and ˜dII. Note that QMPE appears to be unavoidable in the ground state probability ρgg(t) in Fig. 1 i.e. there is no such region in the (d˜ I, d˜ II) where there is no QMPE. B. Energy Here we analyze QMPE in average energy E(t) = Tr[ρb(t)H] where H is the system Hamiltonian presented as a 2 × 2 matrix with elements H11 = 1, H22 = 0, H12 = H21 = ˜d/2 [95]. Consequently, E(t) can be expressed as Note that the average energy directly captures the effect of coherence due to the presence of the off-diagonal density matrix elements in Eq. (18). To explore the QMPE in E(t), we consider the time evolution of the energies starting from the two different initial conditions I and II and look for their intersections during the relaxations. More precisely, we would like to find the solutions to ∆E(t) = 0 where ∆E(t) = EI(t) − EII(t). We obtain the following expression where the elements α1, α2, α3 are given by Eq. (15). Note that Eq. (20) is a similar kind of transcendental equation as Eq. (14), only with different coefficients. Therefore, we can analyze the possibility of QMPE in energy using Lambert W functions as we did for the ground state probability. The two possible solutions to ∆E(t) = 0 are given by where the superscript E denotes energy and is used to differentiate the solutions from Eq. (17) obtained for ground state probability. Figure 2 exhibits the solutions in Eq. (21) as functions of the control parameters ( ˜dI, ˜dII). We observe region with single QMPE (only t˜E 0 exists) as well as region for double QMPE (both t˜E 0 and t˜E −1 exist). In addition, we observe regions where both the solutions are absent i.e. no QMPE. This crucial difference in Fig. 2 compared to Fig. 1 indicates that QMPE in ground state probability does not necessarily implies QMPE in average energy. However, Figs. 1 and 2 also show that there are common parameter values that lead to QMPE both in ρgg(t) and E(t). We present an explicit example in Fig. 3 where both of these quantities exhibit double QMPE, for the same set of parameters. However, the two intersection times for ρgg(t) and E(t) are different from each other. The intersection times for ρgg(t) are comparatively far from each other with the second QMPE being much weaker in magnitude [Fig. 3(a)], whereas the intersection times for E(t) are comparatively much closer to each other, with both the first and second QMPE being comparable in magnitude [Fig. 3(b)]. C. von Neumann entropy Next we consider the von Neumann entropy SvN(t), which is a function of ρb(t), defined as The nonlinearity of SvN(t) as a function of the density matrix elements poses difficulties to perform analytical treatment of the QMPE in von Neumann entropy, in contrast to what we could achieve previously for ground state probability and energy. To observe QMPE in SvN(t), we consider the intersection time(s) for the quantity ∆SvN(t) = S I vN −S II vN. In Fig. 4, we present the behavior of ∆SvN(t) for the same set of parameter values used in Fig. 3. Indeed, we observe that ∆SvN(t) intersects zero twice, implying the existence of double QMPE in the von Neumann entropy, similar to ρgg(t) and E(t). D. Temperature We use the definition of temperature T (t) following Ref. [65] to examine the possibility of thermal QMPE in Hatano’s model, as given below where E(t) and SvN(t) are defined in Eqs. (18) and (22), respectively. The QMPE in system temperature has been observed recently in Ref. [65] using a semi-classical approach. Naturally, it would be interesting to explore thermal QMPE in Hatano’s model that involves the effect of off-diagonal density matrix elements. However, we find that there is no thermal QMPE for the same set of parameters that have produced QMPE for ground state probability [Fig. 3(a)], energy [Fig. 3(b)] and entropy [Fig. 4]. To search for thermal QMPE, we consider a variable dissipation protocol that allows us to use Γ˜ I 6= Γ˜ II 6= Γ˜ (wider parameter space compared to the fixed dissipation protocol Γ˜ I = Γ˜ II = Γ). Indeed, using such protocol, we ˜ have obtained the thermal QMPE shown in Fig. 5 where we present the temperature difference ∆T (t) between two copies I and II. We observe that ∆T (t) becomes zero at some intermediate relaxation time that denotes the intersection of two copies, after which the initially hotter copy becomes colder, leading to thermal QMPE. E. Kullback-Leibler divergence The Kullback-Leibler (KL) divergence is a measure of difference from steady state which is a monotonically decreasing function of time, and it is given by To obtain QMPE in KL divergence, we use the variable dissipation protocol. In Fig. 6(a) we present an example where the copy starting at a higher distance from steady state relaxes faster compared to the other copy with lower initial distance. The copies I and II intersect each other leading to QMPE in DKL(t) and the copy starting at higher initial distance reaches the steady state faster. We have not observed multiple QMPE in KL divergence. It would be an interesting future question to understand if the absence of multiple QMPE in DKL(t) is its generic feature and if the reason is its monotonicity. An important variable of interest in any relaxation process, be it classical or quantum, is the speed of the relaxation. Since the KL divergence gives the distance measure from steady state, a relevant relaxation speed can be defined as where the negative sign is used to complement the fact that the KL divergence decreases with time. It would be interesting to see if the QMPE in DKL(t) is also manifested in the speed vKL(t). For the same set of parameters that produced QMPE in KL divergence [Fig. 6(a)], we investigate QMPE in the speed in Fig. 6(b). Indeed, we observe that the speeds of the two copies I and II intersect each other, leading to QMPE. The higher distant copy goes towards steady state with a higher speed and after the intersection its speed becomes lower. Notably, the intersection times for DKL(t) and vKL(t) are different. In fact, there is a time window where the initially higher distant copy becomes lower distant copy but still maintains its higher speed for some time. It would be intriguing to investigate whether there is some general upper bound on the relaxation speed vKL(t). We end this section by mentioning that the analysis of the QMPE in region (c) would follow similar manner as in region (d) discussed elaborately in the present section. This is because region (c) is also a line of second-order EP just like region (d), the only difference is that the arrangement of eigenvalues for region (c) obeys 0 < λ2 < λ3 = λ4.

Multiple Quantum Mpemba Effect: Exceptional Points and Oscillations: QMPE in Region

Let’s talk about how to add icons to a website from . Figma Checking the layouts created by layout artists and developers, I often encounter issues with adding icons to a website, which can result in an awkward appearance. Icons are sometimes added as PNG images or replaced with similar ones from Font Awesome. Trust me, navigating this situation can be quite challenging. HTML In this regard, I would like to make sense of it and share, in sufficient detail, how best to add icons to your website from the Figma layout. There are plenty of ways, and the choice of a specific method depends on the project itself and the designer who is responsible for the layout. This is what we are going to talk about! Method #1: Export the Icons to an SVG Format and Embed Them Either As Code, as an Image or as a CSS Property For this, we select the icon in Figma and click on export to SVG format, save it to our project, and paste it into our website: 
<img src="image/icons/icons-refresh.svg" alt="">


<svg width="23" height="23" viewBox="0 0 23 23" fill="none" xmlns="http://www.w3.org/2000/svg">
	<path d="m 421.316,734.84 c -325.7222,0 -421.31990625,100.48 -421.31990625,379.19 v 346.8 c 0,278.74 95.59770625,379.22 421.31990625,379.22 H 1192.67 V 1630.99 H 421.316 c -142.597,0 -176.632,-42.15 -176.632,-170.16 v -74.55 H 1186.21 V 1190.21 H 244.684 v -77.78 c 0,-132.899 32.421,-168.539 176.632,-168.539 H 1192.67 V 734.84 H 421.316" fill="white"/>
</svg>


<span class="icons-refresh"></span>

<style>
    .icon-refresh {
        display: inline-block;
        position: relative;
        height: 16px;
        width: 16px;
        background-position: center;
        background-size: 100%;
        background-image: url('image/icons-refresh.svg');
        background-repeat: no-repeat;
    }
</style> This option is obvious, simple, and works well enough. However, there are 2 very unpleasant problems with this method is that if we use svg code, the code itself becomes quite large since the icon can be very complex in terms of colors and the number of vectors inside. The first problem is that if we connect the icon in this way, it becomes problematic for us to change its color through CSS or even JS. We will have to export a duplicate icon with a different color. As for the object tag, the link will not work properly at all if we wrap it in the tag. The second problem <a> There is another : we cannot set the icon size through the font-size property (i.e. we do not inherit the font size). Thus, you have to waste your time on setting width and height. small drawback Perhaps the only is that the option is quite simple and saves a little time for small projects. benefit Therefore, this option is suitable when there are not many icons in the layout (i.e. a maximum of 3-5 pieces) and their colors do not change anywhere (for example, on hover or in a layout theme). Method #2: Create 1 file with all the icons. This can also be referred to as Icon texture. This option also involves exporting icons to svg or png format but in one file, where all the icons will be of the same size. This is a rather old method, but you need to know about it. I know for sure that VKontakte used this method for a very long time. After the redesign, they abandoned this method in favor of SVG + JS. For example, social media icons: Writing the code for the and icons: Facebook Instagram <span class="icons icons_facebook"></span>
<span class="icons icons_instagram"></span>

<style>
    .icons {
        display: inline-block;
        position: relative;
        width: 42px;
        height: 42px;
        background-image: url('imgs/socials-pack.svg');
        background-size: 290px;
        background-repeat: no-repeat;
    }

    .icons_instagram {
        background-position: 240px 0;
    }
</style> Here, we also have 2 problems: , it can take a long time for us to prepare the CSS properties. First is that it is also very problematic to change colors. You will have to export as many duplicate icons as you need colors. The second problem However, there is 1 nice benefit. We get small HTML code since we use only one class to add an icon. The option should be used with caution, taking into account the specifics of the project; for example, if we want to make a plugin with emoticons for a chat). In other situations, you need to use other options. Method #3: Use a ready-made icon service. Here you need to ask your designer where the icons are taken from. Let’s make a brief excursion away from the main topic: you should always ask your designer about what icons you need, where the images come from, where to get the font, etc. It often happens that icons are taken from some service that allows you to add icons through styles or scripts. In Figma, designers often use the Iconify service. Also, some designers use icons that can be exported directly to the website, for example, EvaIcons. An example of exporting from Iconify: <script src="https://code.iconify.design/2/2.1.1/iconify.min.js"></script>
<span class="iconify" data-icon="ci:refresh-01"></span> Here I’m going to start with the pros: firstly, we can easily and quickly add all the icons to the website; secondly, it is easy to change the color of the icon by using the color property in CSS; thirdly, the size of the icon is inherited from the font and we can change it by using the font-size property; and in general, our html code looks neat, the icons occupy 1 line. Perhaps the only is that we connect a third-party service/library, which causes a lot of extra code. However, it’s the little things, especially if the project is on a deadline. This option should be used if there are more than 5 icons in the layout and their colors do not change anywhere. drawback Method #4: Turn all the icons into an icon font In this case, you’re out of luck since the designer used icons that cannot be exported directly to the website (what a bad man). What we do: we export all icons to svg format (it would be better if they were all black, written in the Latin script without any symbols or the word “icon”); we send them to the icomoon service (to save a lot of time). Your icons must be filled. The service does not work with strokes, and it will warn you if some icon is stroked; we generate an icon font and get a ready-made CSS file and a thumbnail with icons. Our code: <link rel="stylesheet" href="fonts/icomoon/style.css">

<i class="icons-refresh"></i>

<button class="button">Button with icon</button>

<style>
    .button:before {
        font-family: 'icomoon', sans-serif;
        content: '\e90a';
    }
</style> I would like to highlight the following pros: the ability to easily change the color by using the CSS  color property; the ability to set the size of the icon by using the font size (i.e. the icon inherits the font size); a small html code since we only use 1 small class to add the icon; simple icon support (to easily add and remove icons from our arsenal); we save space by having only those icons and code that we need (as opposed to using a ready-made solution, where there may be more than a hundred icons). The cons are: we spend a little more time preparing the icon font; it is important for us to ensure that the icons are filled rather than stroked. This option is suitable when there are a lot of icons in the layout and their colors can change. Conclusion My dear friends! Believe me, all this is enough for you to learn how to correctly add any icons to any website. After all, the html layout must match the layout in Figma, while maintaining performance and code quality.

4 Ways to Add Icons from Figma to Your Website

266 Stories To Learn About Product Development

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References 5 Materials and Methods In this section, we endeavor to elucidate key mathematical and computational foundations that are instrumental for the work presented in this study. Specifically, we delve into spectral graph theory, simplicial complex, and persistent Laplacian methods, highlighting their significance in capturing topological and spectral properties essential for the characterization of proteins. Additionally, we discuss machine learning and deep learning paradigms, focusing on their role in processing, analyzing, and interpreting these complex features, especially within the confines of test datasets and validation settings. 5.1 Persistent Laplacian characterization of proteins A simplicial complex is made up of a set of simplices and generalises beyond graph networks at higher dimensions [44, 45, 46, 47]. Every simplex is a finite set of vertices which can be interpreted as the atoms in a protein structure. Essentially, simplices can be a point (0-simplex), an edge (1-simplex), a triangle (2-simplex), a tetrahedron (3-simplex), or in higher dimensions, a p-simplex. In other words, a k-simplex σ k = {v0, v1, · · · , vk} is the convex hull formed by k + 1 affinely independent points v0, v1, · · · , vk as follows, Simplicial complex A geometric simplicial complex K is a finite set of geometric simplexes that satisfy two essential conditions. First, any face of a simplex from K is also in K. Second, the intersection of any two simplexes in K is either empty or shares faces. Commonly used methods to construct simplicial complexes are Cech complex, Vietoris-Rips complex, Alpha complex, Clique ˇ complex, Cubic complex, and Morse complex [44, 45, 46, 47]. In the case k = 0, then L0 = B1B⊤ 1 since ∂0 is a zero map Persistent Laplacian (PL) were first introduced by integrating graph Laplacian and multiscale filtration [25]. Analyzing the spectra of k-combinatorial Laplacian matrix allows both topological and geometric information (i.e. connectivity and robustness of simple graphs) to be obtained. However, this method is genuinely free of metrics or coordinates, which induced too little topological and geometric information that can be used to describe a single configuration. Persistent Laplacian Therefore, PL was extended to simplicial complexes. This allows a sequence of simplicial complexes from a filtration process to generate persistent Laplacian which is largely inspired by persistent homology and in earlier works in multiscale graphs. For the rest of this section, we introduce mainly on the construction of PL. First, a k-combinatorial Laplacian matrix is symmetric and positive semi-definite. Therefore, its eigenvalues are all real and non-negative. The multiplicity of zero spectra (also called harmonic spectra) reveals the topological information, and the geometric information will be preserved in the non-harmonic spectra. This nested sequence of simplicial complexes induces a family of chain complexes In order to illustrate the difference between PL and PH, Figure 5 describes a point cloud, basic simplices, a filtration process and the comparison between persistent Laplacian and persistent homology barcodes of 13 points. The filtration process in Figure 5(c) shows the different stages of a Rips filtration process for the 13 points. Figure 5(d) shows the persistent homology barcodes (in blue) and persistent non-harmonic spectra (in red). It can be seen that the nonharmonic spectra provides the additional homotopic shape evolution that is missing in persistent homology in the later part of the filtration process. 5.2 Persistent Laplacian descriptors where DE(·, ·) is the Euclidean distance between the two atoms and Loc(·) refers to the atom’s location which is either in the mutation site or in the rest of the protein. Here, we construct two types of simplicial complexes in our PL computation, such as Vietoris-Rips complex (VC) and Alpha complex (AC). Both complexes are used to characterize the first order interactions and higher order patterns respectively. To capture and characterize different types of atomatom interactions, we generate the PL based on different atom subsets by selecting one type of atom in the mutation site and one other atom type in the rest of the protein. Different types of atom-atom interactions characterize the different interactions in proteins. For example, interactions generated from carbon atoms are associated with hydrophobic interactions. Similarly, interactions between nitrogen and oxygen atoms correlate to hydrophilic interactions and/or hydrogen bonds. Both types of interactions are illustrated in Figure 6. Interactive PLs have the capability to unveil additional details about bonding interactions and offer a fresh and distinct representation of molecular interactions in proteins. For zero dimensions, we consider both the harmonic spectra and non-harmonic spectra information for each persistent Laplacian. Filtration using Rips complex with DI distance is used. The 0-dimensional PL features are generated from 0A to 6 ˚ A with 0.5 ˚ A gridsize. For the ˚ non-harmonic spectra information, we count the number of non-harmonic spectra and calculate seven statistical values of non-harmonic spectra such as sum, minimum, maximum, mean, standard deviation, variance and the sum of eigenvalues squared. This generates eight statistical values for each of the nine atomic pairs. Therefore, the dimension of 0-dimensional PL features for a protein is 72. In total, the 0-dimensional PL-based feature size after concatenating features at different dimensions for wild type and mutant is 1872. For one or two dimensions, we perform the filtration using Alpha complex with the DE distance. The limited number of atoms in the local protein structure can create only a few high-dimensional simplexes, resulting in minimal alterations in shape. As a result, it suffice to consider features from only harmonic spectra of persistent Laplacians by coding the topological invariants for the high-dimensional interactions. Using GUDHI[51], the persistence of the harmonic spectra can be represented by persistent barcodes. The topological feature vectors are generated by computing the statistics of bar lengths, births and deaths. Bars shorter than 0.1A are excluded as they do not exhibit any clear physical meaning. The remaining bars are ˚ then used for computing the statistics: (1) sum, maximum and mean for lengths of bars; (2) minimum and maximum for the birth values of bars; (3) minimum and maximum for the death values of bars. Each set of point clouds leads to a seven-dimensional vector. These features are calculated on nine single atomic pairs and one heavy atom pair. The dimension of oneand two-dimensional PL feature vectors for a protein is 140. In total, the higher-dimensional PL-based feature size after concatenating features at different dimensions for wild type, mutant and their difference is 420. 5.3 Persistent Homology Persistent homology is part of the harmonic spectra of PL. The homology groups in PH illustrate the persistence of topological invariants, hence providing the harmonic spectral information in PL. The site- and element-specific PH features are generated in a similar way as compared to PL. Similar to PL, filtration construction is also employed to PH. For the zero dimension, the filtration parameter can be discretized into several equally spaced bins, namely [0, 0.5], (0.5, 1], · · · , (5.5, 6]A. The death value of the bars are summed in each bin resulting in 12 ˚ ×18 features. For each bin, we count the numbers of persistent bars, resulting in a nine-dimensional vector for each point cloud. Similarly, this is performed for each of the nine single atomic pairs. Hence, the dimension of PH features for a protein is 216. For one or two dimensions, the identical featurization from the statistics of persistent bars in PH is used. The PH embedding combines features at different dimensions as described above and concatenated for wild type, mutant and their difference, resulting in a 648-dimensional vector 5.4 Transformer Features Recently, we have seen significant advancements in modelling protein properties using largescale protein transformer models trained on hundreds of millions of sequences. These models, like ESM [33] (evolutionary scale modeling) and ProtTrans[34, 35], have demonstrated impressive performance. Moreover, hybrid fine-tuning approaches that leverage both local and global evolutionary data have proven to enhance these models even further. For instance, eUniRep is an improved LSTM-based UniRep model achieved through fine-tuning with knowledge extracted from local multiple sequence alignments (MSAs). Additionally, the ESM model can be fine-tuned using either downstream task data or local MSAs. In our research, we employed the ESM-1b transformer, a model that falls under the transformer architecture. This particular variant was trained on a dataset of 250 million sequences using a masked filling procedure and boasts an architecture comprising 34 layers with a whopping 650 million parameters. The ESM transformer’s primary role in our work was to generate sequence embeddings. At each layer of the ESM model, it encoded a sequence of length L into a matrix sized at 1,280×L, excluding the start and terminal tokens. For our study, we utilized the sequence representation derived from the final (34th) layer and computed the average along the sequence length axis, resulting in a 1,280-component vector. 5.5 Performance Metrics PPV and NPV assesses the true positive and true negative proportion of the predicted results for each solubility class. PPV and NPV are computed based on TP, TN, FP and FN which represents the true positive, true negative, false positive and false negative values for each solubility class. For each solubility class, PPV and NPV can be computed by: This paper is under CC 4.0 license. available on arxiv

Interrogating TopLabGBT's Computational Framework

In a shocking turn of events, the cybersecurity community has been rocked by what is now being dubbed the "Mother of all Breaches" (MOAB) – the largest data breach in history. With a staggering , amounting to an astonishing , exposed to the public, the breach has raised significant concerns about the security of online platforms and the potential risks users now face. 26 billion records 12 terabytes of data In this article, we delve into the details of the breach, the platforms affected, and the potential dangers, and provide recommendations for users to protect themselves. The Scale of the Breach Discovered by cybersecurity researcher Bob Dyachenko and the team at , the massive database was found on an unsecured server, highlighting the sheer scale of the breach. Estimated at 12 terabytes, the exposed data includes sensitive information from a myriad of online platforms, with Chinese giants Tencent and Weibo being the hardest hit. Cybernews Platforms and Data Compromised The impacts a wide range of platforms, with Tencent's QQ and Weibo leading the pack with 1.5 billion and 504 million compromised records, respectively. breach The exposed database includes data encompassing email addresses, phone numbers, images, and user accounts extracted from prominent social platforms like MySpace (360 million files), Twitter (281 million records), Deezer (258 million), LinkedIn (251 million), Adobe (153 million), Canva (143 million), VK (101 million), Telegram (40 million), as well as LinkedIn, Adobe services, Gmail, and Canva. Furthermore, government entities in the United States, Brazil, Turkey, Germany, and the Philippines have also fallen victim to data compromise. Potential Dangers and Repercussions The exposed data poses serious threats to users, including the risk of identity theft, targeted cyberattacks, and credential stuffing. With personal details such as names, email addresses, and phone numbers now in the hands of malicious actors, users are urged to remain vigilant against potential fraudulent activities. In 2019, an unsecured database created by Verifications.io led to the leakage of nearly a billion records, marking one of the largest and most detrimental breaches at that time. However, the scale of data exposed in that incident falls short compared to the massive amount leaked by QQ alone in the current scenario. Recommendations for Users In response to this unprecedented breach, users are advised to take immediate action to protect themselves: Change passwords: Update passwords for all affected platforms and refrain from using the same password across multiple accounts. Enable two-factor authentication: Add an extra layer of security to your accounts wherever possible. Be cautious of phishing attempts: Stay vigilant against phishing emails and avoid clicking on suspicious links or downloading attachments from unknown senders. Monitor accounts: Regularly check accounts for unusual activity and report any suspicious occurrences immediately. The Road Ahead As investigations into the breach continue, this incident serves as a stark reminder of the vulnerabilities in our digital world. Individuals and organizations alike must prioritize cybersecurity measures to mitigate risks associated with large-scale data breaches. Conclusion The "Mother of all Breaches" underscores the evolving nature of the cybersecurity landscape. By adopting proactive security measures and remaining vigilant, users can contribute to minimizing the risks associated with large-scale data breaches. However, it's crucial to stay informed and verify the authenticity of information to prevent the spread of misinformation in an era where fake news can abound. Dedicated individuals have developed a tool to ascertain whether your data has been compromised. Users can utilize this service to search for email addresses and phone numbers, accessing Cybernews' personal data leak checker for added security. Food for Thought This news has been covered by The New York Post, Forbes, Daily Mail, and other top media outlets. However, imagine that it's fake and misinformation. How can you verify that it's all genuine and a fact? Do you think this vast volume of leaked data can become cyber weaponry in the hands of criminals? I'm just giving you food for thought. Additionally, it prompts us to consider the potential weaponization of leaked data by cybercriminals, sparking reflection on the broader implications of such breaches in the digital age. Do you think this vast volume of leaked data can become cyber weaponry in the hands of criminals? I'm just giving you food for thought.

Inside the "Mother of all Breaches" Shaking the Cybersecurity World

HackerNoon editorial team has launched to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. this interview series with women in tech Meet Olga Klochkova, an experienced product management expert who has successfully launched products at major corporations like Yandex and VK despite age-related challenges. In our interview, Olga shares insights into her career, experiences at top companies, and how personal hobbies contribute to professional growth. Join us in exploring the practical side of Olga Klochkova's story, where tenacity and dedication are the driving forces behind her accomplishments. Can you share a bit about yourself? Hi, I'm Olga, and I'm excited to share my journey in the tech industry, where, at the age of almost 25, I've accumulated over 5 years of experience. Currently, I serve as the Head Product Manager of a UGC service with a substantial user base of 30 million MAU. Previously, I worked as a Lead product manager at Yandex. One aspect of my professional journey that I believe sets me apart is the intersection of my age and gender within a leadership position. Being one of the youngest leaders and a woman in the tech space has been both an empowering and challenging experience. It is what I want to unfold here. Why did you choose this field in the first place? As a student pursuing an Applied Mathematics degree, I became disillusioned with my education and opted to learn from the market. At the age of 18, I embarked on various part-time jobs, eventually delving into the field of consultancy. Continuing my journey in the niche consultancy at the age of 19, I initially started by providing support to leaders who drove various big projects. However, it wasn't long before I found myself leading initiatives aimed at helping middle-sized companies identify bottlenecks in their strategy and marketing processes, directly impacting their business metrics. The role demanded an almost 24/7 commitment, often with late-night company meetings. Despite the exhaustion, I was driven by a deep desire to contribute meaningfully, gain valuable experience, and build a foundation of market-based knowledge. Fortunately, the company not only incentivized these aspirations but also refrained from imposing limits on workload. Within just two months, my dedication led to a promotion, elevating me to a position where I led entire client projects and managed a cross-functional team. My team was comprised of a BI analyst and a researcher, forming a dynamic and cross-pollinated group. The responsibilities included tasks reminiscent of product management, such as conducting interviews, analyzing market and client internal processes, and delving into metrics within the client's funnel, operational metrics, and the structure of expenses. This analytical approach allowed us to identify patterns in product indicators, providing valuable insights into our clients' growth potential. After a year of this intense, non-limited workload, I realized the importance of a more stable work-life balance. While the experience and network gained during that period were invaluable, I decided to transition to a role that offered a more sustainable pace. This led me to the position of Product Manager at Practicum, an educational product-led service. Initially, it seemed like an opportunity to slow down, but it quickly became apparent that Practicum operated as a startup within a larger corporation. The dynamic environment presented a new set of challenges and opportunities, allowing me to leverage my skills in a different context. This shift marked a pivotal moment in my career, where I bridged the intensity of my previous role with a more structured and innovative approach to product management. It is how I entered this industry. At Practicum, I led the launch of significant features, including the React and Node.js simulator, driving substantial growth in our technical platform and attracting more users. I actively contributed to various educational and career program courses. A notable achievement was introducing the B2B recruiter solution, which not only addressed industry needs but also created new revenue streams for Practicum. What tech are you most excited about right now and why? Undoubtedly from the current perspective of trends, my primary passion lies with AI as well, given its pervasive influence across various tech fields, transforming them into more effective and customizable landscapes. The evolving capabilities of AI to understand user needs and deliver tailored experiences are reshaping the entire tech industry. Specifically, what fascinates me the most is how AI is revolutionizing the M&E industry and what solutions arise through it, how companies change their perspective on AI content creation. Indeed, while some companies are actively embracing AI to craft personalized and engaging experiences, certain Media and Entertainment (M&E) entities remain committed to relying solely on human creativity. They perceive the irreplaceable value that human input brings to the creation of vibrant and original content. The balance between leveraging AI and preserving the unique touch of human creativity continues to be a nuanced decision within the M&E industry. The era of AI-generated content has not only brought about new possibilities but has also prompted a shift in focus towards human-centric values. This is because there is a lot of information noise in a beautiful wrapper. It is so fascinating to research different viewpoints and changes right now! What are your hobbies and interests outside of tech? Outside of the tech realm, I'm an avid enthusiast of sports, finding joy and balance in physical activities. I dedicate 20 minutes each day to exercise; during the week, I go hiking or running. These activities not only keep me physically fit but also serve as a refreshing break from the digital world. In some kind, I may call my lifestyle a kind of hobby. I am a digital nomad for the past two years, and I've embraced a lifestyle without a permanent residence. During this time, I've explored nearly 16 different countries, allowing me to experience diverse cultures and landscapes. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? Despite my achievements and the high impact of the already launched products in Yandex and UGC-service I currently manage, I've found myself grappling with societal perceptions associated with age and gender. The tech industry, like many others, has its preconceived notions about what a leader should look and sound like. I've often questioned whether my age and the pitch of my voice might overshadow my professional capabilities. It's essential to acknowledge that as a woman in tech, we sometimes face biases that are not strictly professional. The stereotypes around what constitutes a powerful or authoritative voice can be limiting. During job interviews, I've encountered instances of ageism, which added an extra layer to the challenges. It's disconcerting when perceptions about age influence how one is perceived professionally. On one occasion, I even received feedback suggesting that I was asking too much for someone of my age, highlighting the persistence of age-related biases. Acknowledging these challenges is crucial, not just for personal growth but also for dismantling stereotypes that hinder the progress of women in tech. What was the biggest failure that you faced, and how did you manage it? A notable setback I encountered recently revolved around a situation with a designer on my product. Although I wasn't her direct lead, I observed signs of dissatisfaction stemming from uncertainty about her professional development and concerns about salary boosts. She wasn't receiving the necessary feedback and guidance from her designated leader, which led to a growing sense of frustration. In hindsight, I realize that I missed an opportunity to take proactive measures. Recognizing her discontent, I could have initiated a conversation with HR to address the situation and ask about her transition to me directly. As someone familiar with her tasks and accountabilities, I had the potential to influence positive change by taking on a more active role in her professional development. Unfortunately, I didn't express enough empathy and attention during that critical time. By the time I recognized the severity of the situation, the designer had already taken steps toward leaving the company. What's your biggest achievement that you're really proud of? I take great pride in successfully pitching a deck to the vice presidents to secure funding for a pivotal shift in my UGC-product. The process involved meticulous planning, a compelling presentation, and a strategic vision for the future. I have done it, and now, I am currently immersed in the exciting phase of preparing to launch a new media product leveraging the foundation built on the database from our existing product In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? I wish to witness more girls in tech. Notably, I observe a higher representation of young women in IT compared to their older counterparts. It appears that the older generation faced more societal limitations and constraints, contributing to the existing gap. To address this, initiatives promoting inclusivity, mentorship, and dismantling stereotypes are crucial for fostering a more equitable tech landscape. However, it's essential to approach these initiatives with a thoughtful reflection on the fears and concerns that individuals might have. Balancing the push for change with a consideration of these apprehensions can contribute to a more effective and empathetic approach to bridging the gender gap in the tech industry. Do you have any advice for aspiring girls who want to join the field? My advice for aspiring girls looking to join the tech field is . , and it becomes evident that standing out and showcasing passion can set you apart. to be proactive, continuously learn, and initiate ideas I often observe a lack of enthusiasm for self-improvement at work Embrace opportunities to enhance your skills, stay curious, and actively contribute to projects. Demonstrating your passion and dedication can open doors and help you thrive in the dynamic world of technology.

Women in Tech: Olga Klochkova's Journey in Tech Leadership and Navigating Ageism

How to Keep Your Fonts in Embedded SVG

HackerNoon editorial team has launched to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. ! this interview series with women in tech Share your story today Anastasiia Moskovchenko is an expert in product management with extensive experience in corporations and startups. Anastasia has been a mentor and held over 100 lectures about product management. She is also an owner of the community that aims to help women succeed in IT. We asked Anastasiia about her career and experience in such companies as VK and Yandex, product management, the path towards winning success while being a single woman in the team, mentoring, life outside of work, and how hobbies help you grow professionally. How Did You Start Your IT Career? I graduated from the computational mathematics and cybernetics faculty at one of the best universities in the CIS region. Pursuing a tech career was kind of a logical step. After graduation, I joined one of the major IT companies in Russia and supported their IT management school. I participated in the selection process, helped students with tasks and projects, and discussed their next steps. After that, I started to be a mentor and a lecturer for IT professionals. Back then, I prepared more than 100 lessons and graduated around 700 students who successfully passed their interviews and got job offers from top IT companies. What trends in IT do you follow? What in the industry are you most interested in today? I have 2 topics I am passionate about right now. The first is the creator economy and user-generated content. Today, it’s important to know trends in social networks and blogging platforms, both from the author’s and user’s perspectives. And, besides, part of my professional experience was exactly about social media platforms. My second passion is AI. If you are working in product management and IT, you have to know what’s going on in AI. Not because it’s a buzzword, but because these new tools might significantly help you with your tasks. I use a variety of such tools. One example is which disrupts the field of data search. You ask the question, and you get an exact answer and a variety of sources of this information. Perplexity Besides, I am a fan of This is an AI communication coach that tracks your speech during Zoom calls, analyzes voice confidence, eye movement, and such, and provides you with recommendations on how you can improve. Poised. Some of these tools change the game completely, so I do think it’s important to monitor AI and constantly think about how you can improve your work processes with it. Tell us about your hobbies outside of technology. How do they help you in your work? I’ve been dancing professionally since I was 3 years old. Once, I was even on the national hip-hop team and participated in European and World Championships. This hobby impacted my life a lot, and my career too, I think. While working in an IT corporation, I was invited to join a fancy team of the winners of the World Hip-Hop Championship “World of Dance'' in Las Vegas. I used to spend all my time after work and on weekends working out with them. It mastered my stress management - you just can't worry about work stuff all the time because your attention is instantly switched. It also gives you an extra backbone. If something doesn't work out at work, but you have successes in other areas that you love, it's much easier to cope with stress. Two years ago, I decided to take acting classes. There, we did many practices on understanding other people, empathy, communication, and even cognitive skills. It turned out to be an outstanding and very transformative experience. I learned how to create more trusting relationships at work. Besides that, it gave me extra creativity. Back then, I was working at FunCorp, and I was in charge of managing push notifications. I generated an incredible amount of creative ideas. Once, I initiated the testing of a very bold idea with quite daring and even sexualized content. As a result, we grew the CTA by 2 times - not only the colleagues liked my idea, but also our users. Women face various challenges within their careers: glass ceiling, sexism, etc. Have you ever faced such phenomena? In my six years in IT, I've consistently been the only woman in my team and never had a female supervisor. Sometimes, male colleagues might underestimate a female's skills and knowledge, and that is what happened to me. This is a big issue if you are one who should guide and manage the developer’s work. Once, the developers' team intentionally gave me wrong time estimates. In such cases, you have nothing else to do but prove your professionalism. To address this, I demonstrated completing the task in two hours, which earned their respect as a leader. Beyond skill-based challenges, I've faced resistance to my extracurricular activities, like dancing and public speaking. However, it's important to recognize that there will always be detractors, regardless of gender. To overcome these situations, I would advise two strategies. First, show your colleagues that you are one of them - demonstrate your skills and knowledge, common interests or background, and win their respect. Secondly, build relationships a level above with those who are mostly interested in the outcome, and not in the relationships. Being the only female in a team is challenging, but by consistently proving your professionalism, you can be recognized first and foremost for your expertise. Tell us about the most significant projects you’ve worked on? In my career, I've led several pivotal projects, but the most notable was the transformation of the short-form video format for Dzen (one of the major Russian social media platforms). We tackled the challenge of skewed interaction data from oversized clip cards by transitioning to a user-friendly, full-screen vertical video format, and innovatively using machine learning to refine content recommendations, significantly enhancing both user engagement and satisfaction. The User-Generated Content (UCG) platforms are quite hard to work with, as a minor change leads you to reputational losses if users don’t appreciate it. We did about 150 experiments to increase user engagement authentically. The result was extraordinary: a fourfold increase in daily active users (DAU) and time spent in four months. It is important to focus not only on current metrics, but also to see the big picture, to understand how each decision and change will affect the quality of user interaction with your product. Another major milestone was at Yandex. Back then, inside a huge corporation, my team and I created a project that later gave birth to a whole new unit. Amid the onset of COVID-19, our primary focus was on monetizing Yandex Maps. There was a clear need to react to ongoing changes in the world and do something helpful. We came up with the idea of a medication delivery from local pharmacies that would be directly integrated into our maps. In just two months, we engaged around 30,000 pharmacies! Then, we started to create supporting solutions for other small businesses that were also harmed by the pandemic. So, the project began to turn into a separate department within Yandex, focusing on special services for entrepreneurs. What do you think about the gender gap in the IT industry? How is it possible to overcome it? Back in my university years, I was one of a few girls at the Faculty of Computational Mathematics and Cybernetics. One day, I got a free invitation to a large conference on marketing and product management. It turned out that was the way to increase the male audience’ engagement. Of course, today, we are not there anymore - we can see more female public speakers, product managers, and developers at such events. But that’s still far from ideal; I wish we could see more girls in tech. Right now in IT, only a third of the product managers are women. It's great that there are programs for us like Women in Tech, but it's even better if these systems work within companies. Tell me about people in IT or industries that inspire you? I'm always driven by stories when the people working on a product are users of this product themselves and make decisions based on their own experience. This always leads to awesome results. For example, I'm really hooked by the story of the UX lead at YouTube Create - Cielo de la Paz. She is a photographer and used to do specifically mobile photography. In 2015, her work was noticed by Apple, and they used it widely for the promo campaign of the iPhone camera. Then, she created a course on creating content with your phone and was invited to teach it at Stanford. One of YouTube's product directors was studying there at the time. Her expertise impressed him so much that after completing the course, she was invited to become a lead of UX design for YouTube Create. Cielo is not an IT expert, but she is a professional in her field with 10 years of experience. At the same time, while working on the editor, she studied 2 thousand users to understand what problems other content creators face when shooting for YouTube. Now, the company is launching a new AI-powered editor under her leadership. When people aren't just improving products, but they are really that passionate about their field, you get real breakthroughs. And that is why I strive to do things that inspire me. Give advice to girls who are starting in this field? The first thing is to do what you are passionate about. Even if you are just starting your IT journey, but you are very interested in a particular industry, you should go there. In certain cases, your keen interest may be more important to a company than having a solid background. If there is no passion for a particular field yet, I suggest starting to get to know the different communities within IT. Follow different opinion leaders, read the comments under their posts, and get to know their networks to understand who these people are, how they live, etc. Different industries in IT have completely different cultures and vibes. That's why it's important to understand what suits you and what doesn't from the very beginning.

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